CogRad: A Cognitively-Inspired Multi-Agent Framework for Radiology Report Generation

• URL: http://arxiv.org/abs/2607.03853v1
• Date: Sat, 04 Jul 2026 12:43:04 GMT
• Title: CogRad: A Cognitively-Inspired Multi-Agent Framework for Radiology Report Generation
• Abstract summary: CogRad is a framework that structures generation around four stages of a radiologist's reading process.<n>On CheXpert Plus, CogRad attains a BLEU-4 of 0.316 and a CIDEr of 0.322, the best scores among the methods we compare against.
• Score: 5.995766869240907
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated radiology report generation (RRG) can ease radiologist workload, yet most existing systems produce a report in a single forward pass, with no mechanism to check a claim against the image or revisit a finding once stated. We present CogRad, a cognitively inspired multi-agent framework that structures generation around four stages of a radiologist's reading process. A Scout agent discovers anatomical regions directly from image patches via slot attention and assigns region and disease-level triage scores; an Investigator agent concentrates representational capacity on the regions Scout flags as suspicious; a Writer agent compiles these signals into a disease gated visual prefix for a large language model; and a Verifier agent supervises training with a visual entailment loss and, at inference, re-examines its own draft sentence by sentence, regenerating any report it judges insufficiently grounded. On CheXpert Plus, CogRad attains a BLEU-4 of 0.316 and a CIDEr of 0.322, the best scores among the methods we compare against. On IU X-Ray, it attains a BLEU-4 of 0.201 and a CIDEr of 0.724, leading every baseline on every standard NLG metric. We further evaluate CogRad with RadGraph F1, CheXbert F1, and a hallucination analysis to assess clinical accuracy beyond standard text-overlap metrics, complemented by ablation studies and Grad-CAM-based visualizations that characterize each agent's contribution and the model's visual grounding.

Related papers

• RadPRISM: Schema-stratified radiology-report supervision for concept-disentangled image representations and visual grounding [14.036871627954156]
  We introduce RadPRISM, which makes a clinician-defined radiology schema a designated stratification axis.<n>An on-premise large language model extracts per-concept text spans from free-text reports, and each clinical concept is aligned in its own dedicated visual subspace.
  arXiv  Detail & Related papers  (2026-07-31T16:14:24Z)
• Harrison.Rad 1.5 Technical Report: A radiology foundation model that can draft reports from images, priors and clinical context [0.6070952062639758]
  Harrison.Rad 1.5 (HR1.5) is a radiology-specific multimodal large language model that accepts interleaved text and visual inputs.<n>It generates structured and unstructured text across plain-film radiology, spanning computed radiography, chest, musculoskeletal, abdominal, spine, and pelvic x-rays, and mammography.<n>HR1.5 is trained through a three-stage pipeline: domain adaptation of a base language model on radiology reports, contrastive vision-encoder training with curriculum-based hard negatives, and visual-question-answering fine-tuning on multi-turn conversations.
  arXiv  Detail & Related papers  (2026-07-07T06:23:08Z)
• A Reasoning-Enabled Vision-Language Foundation Model for Chest X-ray Interpretation [48.23500302185585]
  We present CheXOne, a reasoning-enabled vision-language model for CXR interpretation.<n>CheXOne jointly generates diagnostic predictions and explicit, clinically grounded reasoning traces.<n>We show that CheXOne-drafted reports are comparable to or better than resident-written reports in 55% of cases.
  arXiv  Detail & Related papers  (2026-04-01T05:19:09Z)
• Eyes on the Image: Gaze Supervised Multimodal Learning for Chest X-ray Diagnosis and Report Generation [1.5087814338685968]
  We propose a two-stage framework that enhances disease classification and region-aware radiology report generation from chest X-rays.<n>In the first stage, we introduce a gaze-guided contrastive learning architecture for disease classification.<n>In the second stage, we present a modular report generation pipeline that extracts confidence-weighted diagnostic keywords.
  arXiv  Detail & Related papers  (2025-08-18T16:42:29Z)
• RadAlign: Advancing Radiology Report Generation with Vision-Language Concept Alignment [10.67889367763112]
  RadAlign is a novel framework that combines the predictive accuracy of vision-language models with the reasoning capabilities of large language models.<n>Our framework maintains strong clinical interpretability while reducing hallucinations, advancing automated medical imaging and report analysis through integrated predictive and generative AI.
  arXiv  Detail & Related papers  (2025-01-13T17:55:32Z)
• CheXalign: Preference fine-tuning in chest X-ray interpretation models without human feedback [10.826651024680169]
  Radiologists play a crucial role in translating medical images into actionable reports.<n>Most current vision-language models (VLMs) in radiology rely solely on supervised fine-tuning.<n>We propose an automated pipeline for preference feedback, focusing on chest X-ray radiology report generation (RRG)<n>Our best-performing setup achieves state-of-the-art CheXbert scores on the MIMIC-CXR dataset for the RRG task.
  arXiv  Detail & Related papers  (2024-10-09T16:07:11Z)
• ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data [115.0747462486285]
  ChatRadio-Valuer is a tailored model for automatic radiology report generation that learns generalizable representations. The clinical dataset utilized in this study encompasses a remarkable total of textbf332,673 observations. ChatRadio-Valuer consistently outperforms state-of-the-art models, especially ChatGPT (GPT-3.5-Turbo) and GPT-4 et al.
  arXiv  Detail & Related papers  (2023-10-08T17:23:17Z)
• You've Got Two Teachers: Co-evolutionary Image and Report Distillation for Semi-supervised Anatomical Abnormality Detection in Chest X-ray [33.272751884183556]
  Experimental results on the public MIMIC-CXR benchmark demonstrate CEIRD's superior performance to several up-to-date weakly and semi-supervised methods.
  arXiv  Detail & Related papers  (2023-07-18T12:18:21Z)
• Act Like a Radiologist: Radiology Report Generation across Anatomical Regions [50.13206214694885]
  X-RGen is a radiologist-minded report generation framework across six anatomical regions. In X-RGen, we seek to mimic the behaviour of human radiologists, breaking them down into four principal phases. We enhance the recognition capacity of the image encoder by analysing images and reports across various regions.
  arXiv  Detail & Related papers  (2023-05-26T07:12:35Z)
• Medical Image Captioning via Generative Pretrained Transformers [57.308920993032274]
  We combine two language models, the Show-Attend-Tell and the GPT-3, to generate comprehensive and descriptive radiology records. The proposed model is tested on two medical datasets, the Open-I, MIMIC-CXR, and the general-purpose MS-COCO.
  arXiv  Detail & Related papers  (2022-09-28T10:27:10Z)
• Radiomics-Guided Global-Local Transformer for Weakly Supervised Pathology Localization in Chest X-Rays [65.88435151891369]
  Radiomics-Guided Transformer (RGT) fuses textitglobal image information with textitlocal knowledge-guided radiomics information. RGT consists of an image Transformer branch, a radiomics Transformer branch, and fusion layers that aggregate image and radiomic information.
  arXiv  Detail & Related papers  (2022-07-10T06:32:56Z)
• Auxiliary Signal-Guided Knowledge Encoder-Decoder for Medical Report Generation [107.3538598876467]
  We propose an Auxiliary Signal-Guided Knowledge-Decoder (ASGK) to mimic radiologists' working patterns. ASGK integrates internal visual feature fusion and external medical linguistic information to guide medical knowledge transfer and learning.
  arXiv  Detail & Related papers  (2020-06-06T01:00:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.